From eb558f9cab149a4ba96614c7aeaa20de1c76b250 Mon Sep 17 00:00:00 2001
From: Siyuan Feng <hzfengsy@sjtu.edu.cn>
Date: Tue, 17 Jan 2023 14:53:02 +0800
Subject: [PATCH] [FIX] Minor Compilation Warning Fixes

This PR fixes some warnings from the latest clang compiler.
---
 include/tvm/tir/function.h            | 8 ++++----
 src/tir/analysis/control_flow_graph.h | 7 ++++---
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/include/tvm/tir/function.h b/include/tvm/tir/function.h
index cf92f97360b1..9f7c0fa16b06 100644
--- a/include/tvm/tir/function.h
+++ b/include/tvm/tir/function.h
@@ -224,14 +224,14 @@ class TensorIntrin : public ObjectRef {
   TVM_DEFINE_OBJECT_REF_METHODS(TensorIntrin, ObjectRef, TensorIntrinNode)
 };
 
-/*
+/*!
  * \brief Specialize parameters of PrimFunc.
  * \param func The PrimFunc to be specialized.
  * \param param_map The mapping from function params to the instance.
  * \return The new function with parameter specialized.
  * \note We can define a Meta TIR function with symbolic shape:
  *
- * \code
+ * \code{.py}
  *  @T.prim_func
  *  def mem_copy(a: T.handle, b: T.handle, m: T.int32, n: T.int32) -> None:
  *      A = T.match_buffer(a, (m, n), "float32")
@@ -244,14 +244,14 @@ class TensorIntrin : public ObjectRef {
  *
  * Then we can make it specialized with given shapes or buffers.
  *
- * \code
+ * \code{.py}
  *  a, _, m, n = mem_copy.params
  *  func = mem_copy.specialize({a: tir.decl_buffer((16, 16))})
  *  # or
  *  func = mem_copy.specialize({n: 16, m: 16})
  * \endcode
  *
- * \code {.language-id}
+ * \code{.py}
  *  @T.prim_func
  *  def mem_copy_16_16(a: T.handle, b: T.handle) -> None:
  *      A = T.match_buffer(a, (16, 16), "float32")
diff --git a/src/tir/analysis/control_flow_graph.h b/src/tir/analysis/control_flow_graph.h
index 590392cf658a..00a6b68ff945 100644
--- a/src/tir/analysis/control_flow_graph.h
+++ b/src/tir/analysis/control_flow_graph.h
@@ -292,7 +292,8 @@ class BufferState {
   std::vector<BufferTouch> constraints_;
 };
 
-/*! \brief Represents the flow of control through a `tir::Stmt`
+/*!
+ * \brief Represents the flow of control through a `tir::Stmt`
  *
  * This class contains an internal representation of the possible
  * control flow that may occur during execution of a `tir::Stmt`.  It
@@ -312,7 +313,7 @@ class BufferState {
  *
  * For example, consider the following PrimFunc
  *
- * ```python
+ * \code{.py}
  * @T.prim_func
  * def func(T.Buffer[16, "float32"]):
  *     for i in T.serial(16):
@@ -320,7 +321,7 @@ class BufferState {
  *              B[i] = i
  *         else:
  *              B[i] = i-8
- * ```
+ * \endcode
  *
  * The control flow graph would have eight control blocks.
  *